Overview

Brought to you by YData

Dataset statistics

Number of variables31
Number of observations45
Missing cells38
Missing cells (%)2.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.0 KiB
Average record size in memory250.8 B

Variable types

Text2
Numeric21
Categorical4
DateTime4

Alerts

astrological_sign is highly overall correlated with birth_monthHigh correlation
birth_month is highly overall correlated with astrological_sign and 1 other fieldsHigh correlation
birth_year is highly overall correlated with death_year and 4 other fieldsHigh correlation
body_mass_index is highly overall correlated with body_mass_index_range and 2 other fieldsHigh correlation
body_mass_index_range is highly overall correlated with birth_month and 3 other fieldsHigh correlation
death_age is highly overall correlated with presidency_begin_age and 2 other fieldsHigh correlation
death_year is highly overall correlated with birth_year and 3 other fieldsHigh correlation
height_cm is highly overall correlated with height_inHigh correlation
height_in is highly overall correlated with height_cmHigh correlation
presidency_begin_age is highly overall correlated with death_age and 1 other fieldsHigh correlation
presidency_end_age is highly overall correlated with death_age and 1 other fieldsHigh correlation
term_begin_day is highly overall correlated with birth_year and 4 other fieldsHigh correlation
term_begin_year is highly overall correlated with birth_year and 4 other fieldsHigh correlation
term_end_day is highly overall correlated with birth_year and 4 other fieldsHigh correlation
term_end_month is highly overall correlated with death_age and 1 other fieldsHigh correlation
term_end_year is highly overall correlated with birth_year and 4 other fieldsHigh correlation
weight_kg is highly overall correlated with body_mass_index and 2 other fieldsHigh correlation
weight_lb is highly overall correlated with body_mass_index and 2 other fieldsHigh correlation
death_day has 6 (13.3%) missing values Missing
death_month has 6 (13.3%) missing values Missing
death_year has 6 (13.3%) missing values Missing
death_date has 6 (13.3%) missing values Missing
death_age has 6 (13.3%) missing values Missing
term_end_day has 1 (2.2%) missing values Missing
term_end_month has 1 (2.2%) missing values Missing
term_end_year has 1 (2.2%) missing values Missing
term_end_date has 1 (2.2%) missing values Missing
presidency_end_age has 1 (2.2%) missing values Missing
corrected_iq has 3 (6.7%) missing values Missing
name has unique values Unique
birth_date has unique values Unique
term_begin_date has unique values Unique

Reproduction

Analysis started2025-06-25 22:36:10.152361
Analysis finished2025-06-25 22:36:22.845837
Duration12.69 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

name
Text

Unique 

Distinct45
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size488.0 B
2025-06-26T04:06:22.936234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length26
Median length22
Mean length18.244444
Min length10

Characters and Unicode

Total characters821
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)100.0%

Sample

1st rowGeorge Washington
2nd rowJohn Adams
3rd rowThomas Jefferson
4th rowJames Madison
5th rowJames Monroe
ValueCountFrequency (%)
james 6
 
5.0%
john 6
 
5.0%
william 4
 
3.3%
george 3
 
2.5%
herbert 2
 
1.7%
adams 2
 
1.7%
jefferson 2
 
1.7%
bush 2
 
1.7%
walker 2
 
1.7%
andrew 2
 
1.7%
Other values (83) 89
74.2%
2025-06-26T04:06:23.069248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
75
 
9.1%
e 68
 
8.3%
a 67
 
8.2%
n 65
 
7.9%
r 63
 
7.7%
o 60
 
7.3%
l 43
 
5.2%
i 42
 
5.1%
s 37
 
4.5%
h 28
 
3.4%
Other values (39) 273
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 821
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
75
 
9.1%
e 68
 
8.3%
a 67
 
8.2%
n 65
 
7.9%
r 63
 
7.7%
o 60
 
7.3%
l 43
 
5.2%
i 42
 
5.1%
s 37
 
4.5%
h 28
 
3.4%
Other values (39) 273
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 821
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
75
 
9.1%
e 68
 
8.3%
a 67
 
8.2%
n 65
 
7.9%
r 63
 
7.7%
o 60
 
7.3%
l 43
 
5.2%
i 42
 
5.1%
s 37
 
4.5%
h 28
 
3.4%
Other values (39) 273
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 821
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
75
 
9.1%
e 68
 
8.3%
a 67
 
8.2%
n 65
 
7.9%
r 63
 
7.7%
o 60
 
7.3%
l 43
 
5.2%
i 42
 
5.1%
s 37
 
4.5%
h 28
 
3.4%
Other values (39) 273
33.3%

height_cm
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)42.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180.15556
Minimum163
Maximum193
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2025-06-26T04:06:23.099180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum163
5-th percentile168.4
Q1175
median182
Q3185
95-th percentile190.6
Maximum193
Range30
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.0290163
Coefficient of variation (CV)0.039016373
Kurtosis-0.4355819
Mean180.15556
Median Absolute Deviation (MAD)5
Skewness-0.32624855
Sum8107
Variance49.407071
MonotonicityNot monotonic
2025-06-26T04:06:23.127093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
183 6
13.3%
188 5
11.1%
182 5
11.1%
173 4
8.9%
178 4
8.9%
185 4
8.9%
180 2
 
4.4%
170 2
 
4.4%
175 2
 
4.4%
168 2
 
4.4%
Other values (9) 9
20.0%
ValueCountFrequency (%)
163 1
 
2.2%
168 2
4.4%
170 2
4.4%
171 1
 
2.2%
173 4
8.9%
174 1
 
2.2%
175 2
4.4%
177 1
 
2.2%
178 4
8.9%
179 1
 
2.2%
ValueCountFrequency (%)
193 1
 
2.2%
192 1
 
2.2%
191 1
 
2.2%
189 1
 
2.2%
188 5
11.1%
185 4
8.9%
183 6
13.3%
182 5
11.1%
180 2
 
4.4%
179 1
 
2.2%

height_in
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)42.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.9
Minimum64
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2025-06-26T04:06:23.153923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum64
5-th percentile66.2
Q169
median71.5
Q373
95-th percentile74.9
Maximum76
Range12
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7851065
Coefficient of variation (CV)0.039282179
Kurtosis-0.40681414
Mean70.9
Median Absolute Deviation (MAD)2
Skewness-0.34342905
Sum3190.5
Variance7.7568182
MonotonicityNot monotonic
2025-06-26T04:06:23.184887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
72 6
13.3%
74 5
11.1%
71.5 5
11.1%
68 4
8.9%
70 4
8.9%
73 4
8.9%
71 2
 
4.4%
67 2
 
4.4%
69 2
 
4.4%
66 2
 
4.4%
Other values (9) 9
20.0%
ValueCountFrequency (%)
64 1
 
2.2%
66 2
4.4%
67 2
4.4%
67.5 1
 
2.2%
68 4
8.9%
68.5 1
 
2.2%
69 2
4.4%
69.5 1
 
2.2%
70 4
8.9%
70.5 1
 
2.2%
ValueCountFrequency (%)
76 1
 
2.2%
75.5 1
 
2.2%
75 1
 
2.2%
74.5 1
 
2.2%
74 5
11.1%
73 4
8.9%
72 6
13.3%
71.5 5
11.1%
71 2
 
4.4%
70.5 1
 
2.2%

weight_kg
Real number (ℝ)

High correlation 

Distinct35
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.864444
Minimum55.3
Maximum154.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2025-06-26T04:06:23.216559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum55.3
5-th percentile66.22
Q175.7
median81.6
Q390.3
95-th percentile107.5
Maximum154.2
Range98.9
Interquartile range (IQR)14.6

Descriptive statistics

Standard deviation16.328519
Coefficient of variation (CV)0.19240707
Kurtosis6.5053105
Mean84.864444
Median Absolute Deviation (MAD)7.2
Skewness1.9157353
Sum3818.9
Variance266.62053
MonotonicityNot monotonic
2025-06-26T04:06:23.248750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
79.4 4
 
8.9%
73.5 2
 
4.4%
83.9 2
 
4.4%
70.8 2
 
4.4%
86.2 2
 
4.4%
107.5 2
 
4.4%
72.6 2
 
4.4%
81.6 2
 
4.4%
95.3 1
 
2.2%
80.3 1
 
2.2%
Other values (25) 25
55.6%
ValueCountFrequency (%)
55.3 1
2.2%
63.5 1
2.2%
65.3 1
2.2%
69.9 1
2.2%
70.8 2
4.4%
72.6 2
4.4%
73.5 2
4.4%
74.4 1
2.2%
75.7 1
2.2%
77.1 1
2.2%
ValueCountFrequency (%)
154.2 1
2.2%
117.9 1
2.2%
107.5 2
4.4%
104.3 1
2.2%
101.6 1
2.2%
101.2 1
2.2%
98.4 1
2.2%
95.3 1
2.2%
92.1 1
2.2%
90.7 1
2.2%

weight_lb
Real number (ℝ)

High correlation 

Distinct35
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean187.08889
Minimum122
Maximum340
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2025-06-26T04:06:23.280864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum122
5-th percentile146
Q1167
median180
Q3199
95-th percentile237
Maximum340
Range218
Interquartile range (IQR)32

Descriptive statistics

Standard deviation36.006832
Coefficient of variation (CV)0.19245842
Kurtosis6.5085991
Mean187.08889
Median Absolute Deviation (MAD)16
Skewness1.9171713
Sum8419
Variance1296.4919
MonotonicityNot monotonic
2025-06-26T04:06:23.313416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
175 4
 
8.9%
162 2
 
4.4%
185 2
 
4.4%
156 2
 
4.4%
190 2
 
4.4%
237 2
 
4.4%
160 2
 
4.4%
180 2
 
4.4%
210 1
 
2.2%
177 1
 
2.2%
Other values (25) 25
55.6%
ValueCountFrequency (%)
122 1
2.2%
140 1
2.2%
144 1
2.2%
154 1
2.2%
156 2
4.4%
160 2
4.4%
162 2
4.4%
164 1
2.2%
167 1
2.2%
170 1
2.2%
ValueCountFrequency (%)
340 1
2.2%
260 1
2.2%
237 2
4.4%
230 1
2.2%
224 1
2.2%
223 1
2.2%
217 1
2.2%
210 1
2.2%
203 1
2.2%
200 1
2.2%

body_mass_index
Real number (ℝ)

High correlation 

Distinct36
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.142222
Minimum18.6
Maximum46.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2025-06-26T04:06:23.343667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18.6
5-th percentile21.38
Q123.4
median25
Q327.1
95-th percentile34.62
Maximum46.6
Range28
Interquartile range (IQR)3.7

Descriptive statistics

Standard deviation4.8189724
Coefficient of variation (CV)0.18433675
Kurtosis6.6214434
Mean26.142222
Median Absolute Deviation (MAD)1.8
Skewness2.1200447
Sum1176.4
Variance23.222495
MonotonicityNot monotonic
2025-06-26T04:06:23.378219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
25.6 3
 
6.7%
23.8 3
 
6.7%
22.5 2
 
4.4%
24.4 2
 
4.4%
26.4 2
 
4.4%
25.7 2
 
4.4%
23.2 2
 
4.4%
33.9 1
 
2.2%
46.6 1
 
2.2%
23.4 1
 
2.2%
Other values (26) 26
57.8%
ValueCountFrequency (%)
18.6 1
2.2%
20.8 1
2.2%
21.3 1
2.2%
21.7 1
2.2%
22.1 1
2.2%
22.3 1
2.2%
22.5 2
4.4%
23 1
2.2%
23.2 2
4.4%
23.4 1
2.2%
ValueCountFrequency (%)
46.6 1
2.2%
36.4 1
2.2%
34.8 1
2.2%
33.9 1
2.2%
31.5 1
2.2%
31.2 1
2.2%
29.5 1
2.2%
29.4 1
2.2%
29 1
2.2%
28.7 1
2.2%

body_mass_index_range
Categorical

High correlation 

Distinct5
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size488.0 B
Normal
22 
Overweight
17 
Obese
Severely Obese
 
1
Morbidly Obese
 
1

Length

Max length14
Median length10
Mean length7.7777778
Min length5

Characters and Unicode

Total characters350
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)4.4%

Sample

1st rowNormal
2nd rowOverweight
3rd rowNormal
4th rowNormal
5th rowOverweight

Common Values

ValueCountFrequency (%)
Normal 22
48.9%
Overweight 17
37.8%
Obese 4
 
8.9%
Severely Obese 1
 
2.2%
Morbidly Obese 1
 
2.2%

Length

2025-06-26T04:06:23.410783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-26T04:06:23.436505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
normal 22
46.8%
overweight 17
36.2%
obese 6
 
12.8%
severely 1
 
2.1%
morbidly 1
 
2.1%

Most occurring characters

ValueCountFrequency (%)
e 49
14.0%
r 41
11.7%
l 24
 
6.9%
O 23
 
6.6%
o 23
 
6.6%
N 22
 
6.3%
m 22
 
6.3%
a 22
 
6.3%
i 18
 
5.1%
v 18
 
5.1%
Other values (11) 88
25.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 350
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 49
14.0%
r 41
11.7%
l 24
 
6.9%
O 23
 
6.6%
o 23
 
6.6%
N 22
 
6.3%
m 22
 
6.3%
a 22
 
6.3%
i 18
 
5.1%
v 18
 
5.1%
Other values (11) 88
25.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 350
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 49
14.0%
r 41
11.7%
l 24
 
6.9%
O 23
 
6.6%
o 23
 
6.6%
N 22
 
6.3%
m 22
 
6.3%
a 22
 
6.3%
i 18
 
5.1%
v 18
 
5.1%
Other values (11) 88
25.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 350
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 49
14.0%
r 41
11.7%
l 24
 
6.9%
O 23
 
6.6%
o 23
 
6.6%
N 22
 
6.3%
m 22
 
6.3%
a 22
 
6.3%
i 18
 
5.1%
v 18
 
5.1%
Other values (11) 88
25.1%

birth_day
Real number (ℝ)

Distinct25
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.977778
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2025-06-26T04:06:23.465099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.4
Q18
median15
Q324
95-th percentile29
Maximum30
Range29
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.3188199
Coefficient of variation (CV)0.58323629
Kurtosis-1.3349957
Mean15.977778
Median Absolute Deviation (MAD)8
Skewness0.071293758
Sum719
Variance86.840404
MonotonicityNot monotonic
2025-06-26T04:06:23.495760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
29 4
 
8.9%
4 3
 
6.7%
14 3
 
6.7%
27 3
 
6.7%
20 2
 
4.4%
12 2
 
4.4%
23 2
 
4.4%
30 2
 
4.4%
2 2
 
4.4%
6 2
 
4.4%
Other values (15) 20
44.4%
ValueCountFrequency (%)
1 1
 
2.2%
2 2
4.4%
4 3
6.7%
5 2
4.4%
6 2
4.4%
7 1
 
2.2%
8 1
 
2.2%
9 2
4.4%
10 1
 
2.2%
11 1
 
2.2%
ValueCountFrequency (%)
30 2
4.4%
29 4
8.9%
28 2
4.4%
27 3
6.7%
24 1
 
2.2%
23 2
4.4%
22 1
 
2.2%
20 2
4.4%
19 2
4.4%
18 1
 
2.2%

birth_month
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6666667
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2025-06-26T04:06:23.523412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q310
95-th percentile11.8
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.6431754
Coefficient of variation (CV)0.54647632
Kurtosis-1.3983309
Mean6.6666667
Median Absolute Deviation (MAD)3
Skewness-0.11181865
Sum300
Variance13.272727
MonotonicityNot monotonic
2025-06-26T04:06:23.549629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10 6
13.3%
11 6
13.3%
8 5
11.1%
2 4
8.9%
4 4
8.9%
3 4
8.9%
1 4
8.9%
7 3
6.7%
12 3
6.7%
6 3
6.7%
Other values (2) 3
6.7%
ValueCountFrequency (%)
1 4
8.9%
2 4
8.9%
3 4
8.9%
4 4
8.9%
5 2
 
4.4%
6 3
6.7%
7 3
6.7%
8 5
11.1%
9 1
 
2.2%
10 6
13.3%
ValueCountFrequency (%)
12 3
6.7%
11 6
13.3%
10 6
13.3%
9 1
 
2.2%
8 5
11.1%
7 3
6.7%
6 3
6.7%
5 2
 
4.4%
4 4
8.9%
3 4
8.9%

birth_year
Real number (ℝ)

High correlation 

Distinct39
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1844.3333
Minimum1732
Maximum1961
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2025-06-26T04:06:23.580283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1732
5-th percentile1744.6
Q11791
median1837
Q31908
95-th percentile1946
Maximum1961
Range229
Interquartile range (IQR)117

Descriptive statistics

Standard deviation65.584782
Coefficient of variation (CV)0.035560157
Kurtosis-1.1121448
Mean1844.3333
Median Absolute Deviation (MAD)53
Skewness0.072842273
Sum82995
Variance4301.3636
MonotonicityNot monotonic
2025-06-26T04:06:23.615939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
1946 3
 
6.7%
1767 2
 
4.4%
1924 2
 
4.4%
1913 2
 
4.4%
1822 2
 
4.4%
1732 1
 
2.2%
1882 1
 
2.2%
1857 1
 
2.2%
1856 1
 
2.2%
1865 1
 
2.2%
Other values (29) 29
64.4%
ValueCountFrequency (%)
1732 1
2.2%
1735 1
2.2%
1743 1
2.2%
1751 1
2.2%
1758 1
2.2%
1767 2
4.4%
1773 1
2.2%
1782 1
2.2%
1784 1
2.2%
1790 1
2.2%
ValueCountFrequency (%)
1961 1
 
2.2%
1946 3
6.7%
1942 1
 
2.2%
1924 2
4.4%
1917 1
 
2.2%
1913 2
4.4%
1911 1
 
2.2%
1908 1
 
2.2%
1890 1
 
2.2%
1884 1
 
2.2%

birth_date
Date

Unique 

Distinct45
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size488.0 B
Minimum1732-02-22 00:00:00
Maximum1961-04-08 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-06-26T04:06:23.652995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:23.694004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
Distinct43
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Memory size488.0 B
2025-06-26T04:06:23.774240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length12
Mean length9.7555556
Min length4

Characters and Unicode

Total characters439
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)91.1%

Sample

1st rowWestmoreland County
2nd rowBraintree
3rd rowShadwell
4th rowPort Conway
5th rowMonroe Hall
ValueCountFrequency (%)
county 3
 
4.8%
braintree 2
 
3.2%
charles 2
 
3.2%
city 2
 
3.2%
summerhill 1
 
1.6%
bend 1
 
1.6%
shadwell 1
 
1.6%
port 1
 
1.6%
conway 1
 
1.6%
monroe 1
 
1.6%
Other values (48) 48
76.2%
2025-06-26T04:06:23.948994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 39
 
8.9%
a 38
 
8.7%
e 37
 
8.4%
l 35
 
8.0%
o 34
 
7.7%
i 30
 
6.8%
r 26
 
5.9%
t 22
 
5.0%
18
 
4.1%
h 13
 
3.0%
Other values (32) 147
33.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 439
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 39
 
8.9%
a 38
 
8.7%
e 37
 
8.4%
l 35
 
8.0%
o 34
 
7.7%
i 30
 
6.8%
r 26
 
5.9%
t 22
 
5.0%
18
 
4.1%
h 13
 
3.0%
Other values (32) 147
33.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 439
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 39
 
8.9%
a 38
 
8.7%
e 37
 
8.4%
l 35
 
8.0%
o 34
 
7.7%
i 30
 
6.8%
r 26
 
5.9%
t 22
 
5.0%
18
 
4.1%
h 13
 
3.0%
Other values (32) 147
33.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 439
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 39
 
8.9%
a 38
 
8.7%
e 37
 
8.4%
l 35
 
8.0%
o 34
 
7.7%
i 30
 
6.8%
r 26
 
5.9%
t 22
 
5.0%
18
 
4.1%
h 13
 
3.0%
Other values (32) 147
33.5%

birth_state
Categorical

Distinct21
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Memory size488.0 B
Virginia
Ohio
New York
Massachusetts
North Carolina
Other values (16)
19 

Length

Max length14
Median length13
Mean length8.3333333
Min length4

Characters and Unicode

Total characters375
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)28.9%

Sample

1st rowVirginia
2nd rowMassachusetts
3rd rowVirginia
4th rowVirginia
5th rowVirginia

Common Values

ValueCountFrequency (%)
Virginia 8
17.8%
Ohio 7
15.6%
New York 5
11.1%
Massachusetts 4
 
8.9%
North Carolina 2
 
4.4%
Pennsylvania 2
 
4.4%
Vermont 2
 
4.4%
Texas 2
 
4.4%
California 1
 
2.2%
Connecticut 1
 
2.2%
Other values (11) 11
24.4%

Length

2025-06-26T04:06:23.984987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
virginia 8
14.5%
new 7
12.7%
ohio 7
12.7%
york 5
 
9.1%
massachusetts 4
 
7.3%
carolina 3
 
5.5%
north 2
 
3.6%
pennsylvania 2
 
3.6%
vermont 2
 
3.6%
texas 2
 
3.6%
Other values (13) 13
23.6%

Most occurring characters

ValueCountFrequency (%)
i 47
 
12.5%
a 39
 
10.4%
s 28
 
7.5%
r 27
 
7.2%
o 26
 
6.9%
n 25
 
6.7%
e 24
 
6.4%
t 16
 
4.3%
h 15
 
4.0%
10
 
2.7%
Other values (28) 118
31.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 375
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 47
 
12.5%
a 39
 
10.4%
s 28
 
7.5%
r 27
 
7.2%
o 26
 
6.9%
n 25
 
6.7%
e 24
 
6.4%
t 16
 
4.3%
h 15
 
4.0%
10
 
2.7%
Other values (28) 118
31.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 375
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 47
 
12.5%
a 39
 
10.4%
s 28
 
7.5%
r 27
 
7.2%
o 26
 
6.9%
n 25
 
6.7%
e 24
 
6.4%
t 16
 
4.3%
h 15
 
4.0%
10
 
2.7%
Other values (28) 118
31.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 375
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 47
 
12.5%
a 39
 
10.4%
s 28
 
7.5%
r 27
 
7.2%
o 26
 
6.9%
n 25
 
6.7%
e 24
 
6.4%
t 16
 
4.3%
h 15
 
4.0%
10
 
2.7%
Other values (28) 118
31.5%

death_day
Real number (ℝ)

Missing 

Distinct23
Distinct (%)59.0%
Missing6
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean14.948718
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2025-06-26T04:06:24.013381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.9
Q17
median15
Q322.5
95-th percentile28.2
Maximum31
Range30
Interquartile range (IQR)15.5

Descriptive statistics

Standard deviation9.0056962
Coefficient of variation (CV)0.60243937
Kurtosis-1.3010406
Mean14.948718
Median Absolute Deviation (MAD)8
Skewness0.091759302
Sum583
Variance81.102564
MonotonicityNot monotonic
2025-06-26T04:06:24.042751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
8 4
 
8.9%
4 4
 
8.9%
22 3
 
6.7%
14 2
 
4.4%
28 2
 
4.4%
23 2
 
4.4%
24 2
 
4.4%
18 2
 
4.4%
15 2
 
4.4%
26 2
 
4.4%
Other values (13) 14
31.1%
(Missing) 6
13.3%
ValueCountFrequency (%)
1 1
 
2.2%
2 1
 
2.2%
3 1
 
2.2%
4 4
8.9%
5 2
4.4%
6 1
 
2.2%
8 4
8.9%
9 1
 
2.2%
12 1
 
2.2%
13 1
 
2.2%
ValueCountFrequency (%)
31 1
 
2.2%
30 1
 
2.2%
28 2
4.4%
26 2
4.4%
24 2
4.4%
23 2
4.4%
22 3
6.7%
20 1
 
2.2%
19 1
 
2.2%
18 2
4.4%

death_month
Real number (ℝ)

Missing 

Distinct12
Distinct (%)30.8%
Missing6
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean6.1538462
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2025-06-26T04:06:24.069527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13.5
median6
Q38.5
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.4605937
Coefficient of variation (CV)0.56234647
Kurtosis-0.916846
Mean6.1538462
Median Absolute Deviation (MAD)3
Skewness0.15649978
Sum240
Variance11.975709
MonotonicityNot monotonic
2025-06-26T04:06:24.096758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 7
15.6%
6 6
13.3%
1 5
11.1%
12 4
8.9%
4 4
8.9%
3 3
6.7%
2 2
 
4.4%
10 2
 
4.4%
9 2
 
4.4%
11 2
 
4.4%
Other values (2) 2
 
4.4%
(Missing) 6
13.3%
ValueCountFrequency (%)
1 5
11.1%
2 2
 
4.4%
3 3
6.7%
4 4
8.9%
5 1
 
2.2%
6 6
13.3%
7 7
15.6%
8 1
 
2.2%
9 2
 
4.4%
10 2
 
4.4%
ValueCountFrequency (%)
12 4
8.9%
11 2
 
4.4%
10 2
 
4.4%
9 2
 
4.4%
8 1
 
2.2%
7 7
15.6%
6 6
13.3%
5 1
 
2.2%
4 4
8.9%
3 3
6.7%

death_year
Real number (ℝ)

High correlation  Missing 

Distinct36
Distinct (%)92.3%
Missing6
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean1900.4615
Minimum1799
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2025-06-26T04:06:24.126926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1799
5-th percentile1826
Q11856
median1886
Q31939
95-th percentile2004.2
Maximum2018
Range219
Interquartile range (IQR)83

Descriptive statistics

Standard deviation57.637637
Coefficient of variation (CV)0.030328231
Kurtosis-0.79281663
Mean1900.4615
Median Absolute Deviation (MAD)38
Skewness0.42618654
Sum74118
Variance3322.0972
MonotonicityNot monotonic
2025-06-26T04:06:24.161756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1826 2
 
4.4%
1862 2
 
4.4%
1901 2
 
4.4%
1945 1
 
2.2%
1919 1
 
2.2%
1930 1
 
2.2%
1924 1
 
2.2%
1923 1
 
2.2%
1933 1
 
2.2%
1964 1
 
2.2%
Other values (26) 26
57.8%
(Missing) 6
 
13.3%
ValueCountFrequency (%)
1799 1
2.2%
1826 2
4.4%
1831 1
2.2%
1836 1
2.2%
1841 1
2.2%
1845 1
2.2%
1848 1
2.2%
1849 1
2.2%
1850 1
2.2%
1862 2
4.4%
ValueCountFrequency (%)
2018 1
2.2%
2006 1
2.2%
2004 1
2.2%
1994 1
2.2%
1973 1
2.2%
1972 1
2.2%
1969 1
2.2%
1964 1
2.2%
1963 1
2.2%
1945 1
2.2%

death_date
Date

Missing 

Distinct38
Distinct (%)97.4%
Missing6
Missing (%)13.3%
Memory size488.0 B
Minimum1799-12-14 00:00:00
Maximum2018-12-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-06-26T04:06:24.194596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:24.231377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=38)

death_age
Real number (ℝ)

High correlation  Missing 

Distinct29
Distinct (%)74.4%
Missing6
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean71.025641
Minimum46
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2025-06-26T04:06:24.265689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile52.6
Q163
median70
Q379.5
95-th percentile93
Maximum94
Range48
Interquartile range (IQR)16.5

Descriptive statistics

Standard deviation12.61471
Coefficient of variation (CV)0.17760783
Kurtosis-0.68626546
Mean71.025641
Median Absolute Deviation (MAD)9
Skewness0.15451159
Sum2770
Variance159.1309
MonotonicityNot monotonic
2025-06-26T04:06:24.296567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
67 3
 
6.7%
57 2
 
4.4%
93 2
 
4.4%
90 2
 
4.4%
78 2
 
4.4%
64 2
 
4.4%
63 2
 
4.4%
71 2
 
4.4%
60 2
 
4.4%
49 1
 
2.2%
Other values (19) 19
42.2%
(Missing) 6
 
13.3%
ValueCountFrequency (%)
46 1
2.2%
49 1
2.2%
53 1
2.2%
56 1
2.2%
57 2
4.4%
58 1
2.2%
60 2
4.4%
63 2
4.4%
64 2
4.4%
65 1
2.2%
ValueCountFrequency (%)
94 1
2.2%
93 2
4.4%
90 2
4.4%
88 1
2.2%
85 1
2.2%
83 1
2.2%
81 1
2.2%
80 1
2.2%
79 1
2.2%
78 2
4.4%

astrological_sign
Categorical

High correlation 

Distinct12
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Memory size488.0 B
Scorpio
Aquarius
Pisces
Taurus
Capricorn
Other values (7)
22 

Length

Max length11
Median length9
Mean length6.6
Min length3

Characters and Unicode

Total characters297
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPisces
2nd rowScorpio
3rd rowAries
4th rowPisces
5th rowTaurus

Common Values

ValueCountFrequency (%)
Scorpio 6
13.3%
Aquarius 5
11.1%
Pisces 4
8.9%
Taurus 4
8.9%
Capricorn 4
8.9%
Libran 4
8.9%
Leo 4
8.9%
Gemini 4
8.9%
Cancer 3
6.7%
Sagittarius 3
6.7%
Other values (2) 4
8.9%

Length

2025-06-26T04:06:24.331208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
scorpio 6
13.3%
aquarius 5
11.1%
pisces 4
8.9%
taurus 4
8.9%
capricorn 4
8.9%
libran 4
8.9%
leo 4
8.9%
gemini 4
8.9%
cancer 3
6.7%
sagittarius 3
6.7%
Other values (2) 4
8.9%

Most occurring characters

ValueCountFrequency (%)
i 41
13.8%
r 37
12.5%
a 26
 
8.8%
s 22
 
7.4%
o 22
 
7.4%
u 21
 
7.1%
e 17
 
5.7%
c 17
 
5.7%
n 15
 
5.1%
p 10
 
3.4%
Other values (13) 69
23.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 297
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 41
13.8%
r 37
12.5%
a 26
 
8.8%
s 22
 
7.4%
o 22
 
7.4%
u 21
 
7.1%
e 17
 
5.7%
c 17
 
5.7%
n 15
 
5.1%
p 10
 
3.4%
Other values (13) 69
23.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 297
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 41
13.8%
r 37
12.5%
a 26
 
8.8%
s 22
 
7.4%
o 22
 
7.4%
u 21
 
7.1%
e 17
 
5.7%
c 17
 
5.7%
n 15
 
5.1%
p 10
 
3.4%
Other values (13) 69
23.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 297
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 41
13.8%
r 37
12.5%
a 26
 
8.8%
s 22
 
7.4%
o 22
 
7.4%
u 21
 
7.1%
e 17
 
5.7%
c 17
 
5.7%
n 15
 
5.1%
p 10
 
3.4%
Other values (13) 69
23.2%

term_begin_day
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.044444
Minimum2
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2025-06-26T04:06:24.357010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q14
median4
Q320
95-th percentile20
Maximum30
Range28
Interquartile range (IQR)16

Descriptive statistics

Standard deviation7.8854168
Coefficient of variation (CV)0.78505256
Kurtosis-0.97958101
Mean10.044444
Median Absolute Deviation (MAD)0
Skewness0.74582754
Sum452
Variance62.179798
MonotonicityNot monotonic
2025-06-26T04:06:24.380749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4 25
55.6%
20 11
24.4%
9 2
 
4.4%
30 1
 
2.2%
15 1
 
2.2%
19 1
 
2.2%
14 1
 
2.2%
2 1
 
2.2%
12 1
 
2.2%
22 1
 
2.2%
ValueCountFrequency (%)
2 1
 
2.2%
4 25
55.6%
9 2
 
4.4%
12 1
 
2.2%
14 1
 
2.2%
15 1
 
2.2%
19 1
 
2.2%
20 11
24.4%
22 1
 
2.2%
30 1
 
2.2%
ValueCountFrequency (%)
30 1
 
2.2%
22 1
 
2.2%
20 11
24.4%
19 1
 
2.2%
15 1
 
2.2%
14 1
 
2.2%
12 1
 
2.2%
9 2
 
4.4%
4 25
55.6%
2 1
 
2.2%

term_begin_month
Real number (ℝ)

Distinct7
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3555556
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2025-06-26T04:06:24.404193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median3
Q33
95-th percentile8.8
Maximum11
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.3563797
Coefficient of variation (CV)0.70223236
Kurtosis2.6071021
Mean3.3555556
Median Absolute Deviation (MAD)0
Skewness1.6567404
Sum151
Variance5.5525253
MonotonicityNot monotonic
2025-06-26T04:06:24.427351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 24
53.3%
1 11
24.4%
4 4
 
8.9%
9 2
 
4.4%
8 2
 
4.4%
7 1
 
2.2%
11 1
 
2.2%
ValueCountFrequency (%)
1 11
24.4%
3 24
53.3%
4 4
 
8.9%
7 1
 
2.2%
8 2
 
4.4%
9 2
 
4.4%
11 1
 
2.2%
ValueCountFrequency (%)
11 1
 
2.2%
9 2
 
4.4%
8 2
 
4.4%
7 1
 
2.2%
4 4
 
8.9%
3 24
53.3%
1 11
24.4%

term_begin_year
Real number (ℝ)

High correlation 

Distinct43
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1900.6
Minimum1789
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2025-06-26T04:06:24.457570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1789
5-th percentile1802.6
Q11849
median1889
Q31961
95-th percentile2007.4
Maximum2021
Range232
Interquartile range (IQR)112

Descriptive statistics

Standard deviation66.120207
Coefficient of variation (CV)0.034789123
Kurtosis-1.0865039
Mean1900.6
Median Absolute Deviation (MAD)48
Skewness0.19898509
Sum85527
Variance4371.8818
MonotonicityIncreasing
2025-06-26T04:06:24.494736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1881 2
 
4.4%
1841 2
 
4.4%
1789 1
 
2.2%
1963 1
 
2.2%
1921 1
 
2.2%
1923 1
 
2.2%
1929 1
 
2.2%
1933 1
 
2.2%
1945 1
 
2.2%
1953 1
 
2.2%
Other values (33) 33
73.3%
ValueCountFrequency (%)
1789 1
2.2%
1797 1
2.2%
1801 1
2.2%
1809 1
2.2%
1817 1
2.2%
1825 1
2.2%
1829 1
2.2%
1837 1
2.2%
1841 2
4.4%
1845 1
2.2%
ValueCountFrequency (%)
2021 1
2.2%
2017 1
2.2%
2009 1
2.2%
2001 1
2.2%
1993 1
2.2%
1989 1
2.2%
1981 1
2.2%
1977 1
2.2%
1974 1
2.2%
1969 1
2.2%

term_begin_date
Date

Unique 

Distinct45
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size488.0 B
Minimum1789-04-30 00:00:00
Maximum2021-01-20 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-06-26T04:06:24.530788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:24.573370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)

term_end_day
Real number (ℝ)

High correlation  Missing 

Distinct9
Distinct (%)20.5%
Missing1
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean9.5909091
Minimum2
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2025-06-26T04:06:24.605794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q14
median4
Q320
95-th percentile20
Maximum22
Range20
Interquartile range (IQR)16

Descriptive statistics

Standard deviation7.3589628
Coefficient of variation (CV)0.76728522
Kurtosis-1.4915848
Mean9.5909091
Median Absolute Deviation (MAD)0
Skewness0.64807788
Sum422
Variance54.154334
MonotonicityNot monotonic
2025-06-26T04:06:24.631978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4 25
55.6%
20 11
24.4%
9 2
 
4.4%
15 1
 
2.2%
19 1
 
2.2%
14 1
 
2.2%
2 1
 
2.2%
12 1
 
2.2%
22 1
 
2.2%
(Missing) 1
 
2.2%
ValueCountFrequency (%)
2 1
 
2.2%
4 25
55.6%
9 2
 
4.4%
12 1
 
2.2%
14 1
 
2.2%
15 1
 
2.2%
19 1
 
2.2%
20 11
24.4%
22 1
 
2.2%
ValueCountFrequency (%)
22 1
 
2.2%
20 11
24.4%
19 1
 
2.2%
15 1
 
2.2%
14 1
 
2.2%
12 1
 
2.2%
9 2
 
4.4%
4 25
55.6%
2 1
 
2.2%

term_end_month
Real number (ℝ)

High correlation  Missing 

Distinct7
Distinct (%)15.9%
Missing1
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean3.3409091
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2025-06-26T04:06:24.657282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12.5
median3
Q33
95-th percentile8.85
Maximum11
Range10
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation2.3815491
Coefficient of variation (CV)0.71284461
Kurtosis2.5519463
Mean3.3409091
Median Absolute Deviation (MAD)0
Skewness1.6626697
Sum147
Variance5.6717759
MonotonicityNot monotonic
2025-06-26T04:06:24.680384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 24
53.3%
1 11
24.4%
4 3
 
6.7%
9 2
 
4.4%
8 2
 
4.4%
7 1
 
2.2%
11 1
 
2.2%
(Missing) 1
 
2.2%
ValueCountFrequency (%)
1 11
24.4%
3 24
53.3%
4 3
 
6.7%
7 1
 
2.2%
8 2
 
4.4%
9 2
 
4.4%
11 1
 
2.2%
ValueCountFrequency (%)
11 1
 
2.2%
9 2
 
4.4%
8 2
 
4.4%
7 1
 
2.2%
4 3
 
6.7%
3 24
53.3%
1 11
24.4%

term_end_year
Real number (ℝ)

High correlation  Missing 

Distinct42
Distinct (%)95.5%
Missing1
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean1903.2273
Minimum1797
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2025-06-26T04:06:24.710655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1797
5-th percentile1810.2
Q11849.75
median1895
Q31961.5
95-th percentile2007.8
Maximum2021
Range224
Interquartile range (IQR)111.75

Descriptive statistics

Standard deviation64.614731
Coefficient of variation (CV)0.033950087
Kurtosis-1.1123829
Mean1903.2273
Median Absolute Deviation (MAD)52
Skewness0.2161744
Sum83742
Variance4175.0634
MonotonicityNot monotonic
2025-06-26T04:06:24.747313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1881 2
 
4.4%
1841 2
 
4.4%
1797 1
 
2.2%
1969 1
 
2.2%
1923 1
 
2.2%
1929 1
 
2.2%
1933 1
 
2.2%
1945 1
 
2.2%
1953 1
 
2.2%
1961 1
 
2.2%
Other values (32) 32
71.1%
ValueCountFrequency (%)
1797 1
2.2%
1801 1
2.2%
1809 1
2.2%
1817 1
2.2%
1825 1
2.2%
1829 1
2.2%
1837 1
2.2%
1841 2
4.4%
1845 1
2.2%
1849 1
2.2%
ValueCountFrequency (%)
2021 1
2.2%
2017 1
2.2%
2009 1
2.2%
2001 1
2.2%
1993 1
2.2%
1989 1
2.2%
1981 1
2.2%
1977 1
2.2%
1974 1
2.2%
1969 1
2.2%

term_end_date
Date

Missing 

Distinct44
Distinct (%)100.0%
Missing1
Missing (%)2.2%
Memory size488.0 B
Minimum1797-04-03 00:00:00
Maximum2021-01-20 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-06-26T04:06:24.781999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:24.819619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)

presidency_begin_age
Real number (ℝ)

High correlation 

Distinct23
Distinct (%)51.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.511111
Minimum42
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2025-06-26T04:06:24.852147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum42
5-th percentile46
Q151
median55
Q360
95-th percentile68.8
Maximum78
Range36
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.4334081
Coefficient of variation (CV)0.13390847
Kurtosis0.8412198
Mean55.511111
Median Absolute Deviation (MAD)4
Skewness0.73535971
Sum2498
Variance55.255556
MonotonicityNot monotonic
2025-06-26T04:06:24.882115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
54 5
 
11.1%
51 5
 
11.1%
57 4
 
8.9%
61 3
 
6.7%
55 3
 
6.7%
56 3
 
6.7%
47 2
 
4.4%
46 2
 
4.4%
52 2
 
4.4%
64 2
 
4.4%
Other values (13) 14
31.1%
ValueCountFrequency (%)
42 1
 
2.2%
43 1
 
2.2%
46 2
 
4.4%
47 2
 
4.4%
48 1
 
2.2%
49 2
 
4.4%
50 1
 
2.2%
51 5
11.1%
52 2
 
4.4%
54 5
11.1%
ValueCountFrequency (%)
78 1
 
2.2%
70 1
 
2.2%
69 1
 
2.2%
68 1
 
2.2%
65 1
 
2.2%
64 2
4.4%
62 1
 
2.2%
61 3
6.7%
60 1
 
2.2%
58 1
 
2.2%

presidency_end_age
Real number (ℝ)

High correlation  Missing 

Distinct22
Distinct (%)50.0%
Missing1
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean60.068182
Minimum46
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2025-06-26T04:06:24.976276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile50.3
Q155
median59
Q365
95-th percentile69.85
Maximum77
Range31
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.8823932
Coefficient of variation (CV)0.11457635
Kurtosis-0.31643357
Mean60.068182
Median Absolute Deviation (MAD)5
Skewness0.30897668
Sum2643
Variance47.367336
MonotonicityNot monotonic
2025-06-26T04:06:25.005420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
65 5
 
11.1%
58 4
 
8.9%
56 3
 
6.7%
55 3
 
6.7%
68 3
 
6.7%
54 3
 
6.7%
59 2
 
4.4%
60 2
 
4.4%
52 2
 
4.4%
63 2
 
4.4%
Other values (12) 15
33.3%
ValueCountFrequency (%)
46 1
 
2.2%
49 1
 
2.2%
50 1
 
2.2%
52 2
4.4%
53 2
4.4%
54 3
6.7%
55 3
6.7%
56 3
6.7%
57 1
 
2.2%
58 4
8.9%
ValueCountFrequency (%)
77 1
 
2.2%
74 1
 
2.2%
70 1
 
2.2%
69 2
 
4.4%
68 3
6.7%
66 1
 
2.2%
65 5
11.1%
64 1
 
2.2%
63 2
 
4.4%
61 2
 
4.4%

political_party
Categorical

Distinct7
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Memory size488.0 B
Republican
19 
Democrat
15 
Democratic-Republican
Whig
Unaffiliated
 
1
Other values (2)

Length

Max length21
Median length14
Mean length9.9111111
Min length4

Characters and Unicode

Total characters446
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)6.7%

Sample

1st rowUnaffiliated
2nd rowFederalist
3rd rowDemocratic-Republican
4th rowDemocratic-Republican
5th rowDemocratic-Republican

Common Values

ValueCountFrequency (%)
Republican 19
42.2%
Democrat 15
33.3%
Democratic-Republican 4
 
8.9%
Whig 4
 
8.9%
Unaffiliated 1
 
2.2%
Federalist 1
 
2.2%
National Union 1
 
2.2%

Length

2025-06-26T04:06:25.037015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-26T04:06:25.062616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
republican 19
41.3%
democrat 15
32.6%
democratic-republican 4
 
8.7%
whig 4
 
8.7%
unaffiliated 1
 
2.2%
federalist 1
 
2.2%
national 1
 
2.2%
union 1
 
2.2%

Most occurring characters

ValueCountFrequency (%)
a 47
 
10.5%
c 46
 
10.3%
e 45
 
10.1%
i 36
 
8.1%
n 27
 
6.1%
l 26
 
5.8%
R 23
 
5.2%
p 23
 
5.2%
u 23
 
5.2%
b 23
 
5.2%
Other values (16) 127
28.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 446
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 47
 
10.5%
c 46
 
10.3%
e 45
 
10.1%
i 36
 
8.1%
n 27
 
6.1%
l 26
 
5.8%
R 23
 
5.2%
p 23
 
5.2%
u 23
 
5.2%
b 23
 
5.2%
Other values (16) 127
28.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 446
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 47
 
10.5%
c 46
 
10.3%
e 45
 
10.1%
i 36
 
8.1%
n 27
 
6.1%
l 26
 
5.8%
R 23
 
5.2%
p 23
 
5.2%
u 23
 
5.2%
b 23
 
5.2%
Other values (16) 127
28.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 446
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 47
 
10.5%
c 46
 
10.3%
e 45
 
10.1%
i 36
 
8.1%
n 27
 
6.1%
l 26
 
5.8%
R 23
 
5.2%
p 23
 
5.2%
u 23
 
5.2%
b 23
 
5.2%
Other values (16) 127
28.5%

corrected_iq
Real number (ℝ)

Missing 

Distinct21
Distinct (%)50.0%
Missing3
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean146.83333
Minimum130
Maximum175
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2025-06-26T04:06:25.094572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum130
5-th percentile139.05
Q1140.25
median145
Q3151.75
95-th percentile160
Maximum175
Range45
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation8.2430064
Coefficient of variation (CV)0.056138523
Kurtosis2.1090053
Mean146.83333
Median Absolute Deviation (MAD)5
Skewness1.1281188
Sum6167
Variance67.947154
MonotonicityNot monotonic
2025-06-26T04:06:25.123341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
140 8
17.8%
143 4
 
8.9%
145 3
 
6.7%
142 3
 
6.7%
146 3
 
6.7%
160 3
 
6.7%
139 2
 
4.4%
155 2
 
4.4%
152 2
 
4.4%
175 1
 
2.2%
Other values (11) 11
24.4%
(Missing) 3
 
6.7%
ValueCountFrequency (%)
130 1
 
2.2%
139 2
 
4.4%
140 8
17.8%
141 1
 
2.2%
142 3
 
6.7%
143 4
8.9%
144 1
 
2.2%
145 3
 
6.7%
146 3
 
6.7%
147 1
 
2.2%
ValueCountFrequency (%)
175 1
 
2.2%
160 3
6.7%
159 1
 
2.2%
157 1
 
2.2%
155 2
4.4%
153 1
 
2.2%
152 2
4.4%
151 1
 
2.2%
150 1
 
2.2%
149 1
 
2.2%

Interactions

2025-06-26T04:06:21.586558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:10.753370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:11.336379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:11.846273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:12.389468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:12.872037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:13.441056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:13.947376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:14.505940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:15.005162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:15.636523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:16.155802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:16.729085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:17.250051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:17.750673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:18.301602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:18.801546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:19.424763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:19.916208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:20.485934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:21.000091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:21.618238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:10.794857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:11.360698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:11.869699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:12.412591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:12.895687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:13.465031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:13.970617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:14.528659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:15.029967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:15.662009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:16.180204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:16.752391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:17.273779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:17.841487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:18.324904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-26T04:06:18.778856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:19.398123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:19.894429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:20.462902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:20.977276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-26T04:06:21.561639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-26T04:06:25.161745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
astrological_signbirth_daybirth_monthbirth_statebirth_yearbody_mass_indexbody_mass_index_rangecorrected_iqdeath_agedeath_daydeath_monthdeath_yearheight_cmheight_inpolitical_partypresidency_begin_agepresidency_end_ageterm_begin_dayterm_begin_monthterm_begin_yearterm_end_dayterm_end_monthterm_end_yearweight_kgweight_lb
astrological_sign1.0000.2400.7310.0000.1200.1820.2780.0000.0000.0980.2240.0000.0000.0000.0000.0000.0000.2690.3710.0000.0000.2430.0000.2590.259
birth_day0.2401.000-0.0820.197-0.2150.0410.2140.050-0.171-0.0450.004-0.1330.0420.0420.000-0.0040.069-0.0870.164-0.207-0.0300.273-0.2350.1290.129
birth_month0.731-0.0821.0000.0000.0920.2880.5110.047-0.2010.0500.0510.028-0.229-0.2290.000-0.087-0.266-0.0270.0120.096-0.131-0.1260.0540.0490.049
birth_state0.0000.1970.0001.0000.2340.0000.0000.0000.1710.0000.0000.2930.0000.0000.0000.0390.1440.2770.1540.2800.0000.0000.2730.0000.000
birth_year0.120-0.2150.0920.2341.0000.1160.000-0.123-0.0440.2890.0190.9840.3430.3430.312-0.123-0.1720.593-0.4620.9930.720-0.4210.9950.2730.273
body_mass_index0.1820.0410.2880.0000.1161.0000.909-0.037-0.025-0.002-0.0150.108-0.170-0.1700.000-0.006-0.016-0.043-0.0440.1230.093-0.0520.1400.7880.788
body_mass_index_range0.2780.2140.5110.0000.0000.9091.0000.0000.0000.0000.0000.0000.2290.2420.0000.0000.0000.0000.0000.1540.0000.0860.1640.7200.720
corrected_iq0.0000.0500.0470.000-0.123-0.0370.0001.000-0.077-0.037-0.243-0.238-0.035-0.0350.116-0.267-0.198-0.058-0.138-0.1650.0110.157-0.166-0.015-0.015
death_age0.000-0.171-0.2010.171-0.044-0.0250.000-0.0771.0000.1700.0930.073-0.080-0.0800.1800.6420.6530.150-0.210-0.0070.070-0.582-0.002-0.063-0.063
death_day0.098-0.0450.0500.0000.289-0.0020.000-0.0370.1701.0000.2180.304-0.100-0.1000.053-0.096-0.0630.393-0.0220.2540.421-0.2690.261-0.056-0.056
death_month0.2240.0040.0510.0000.019-0.0150.000-0.2430.0930.2181.000-0.0080.1530.1530.0000.1180.0610.240-0.0800.0090.1960.0350.0090.0760.076
death_year0.000-0.1330.0280.2930.9840.1080.000-0.2380.0730.304-0.0081.0000.2500.2500.391-0.083-0.0480.423-0.1140.9890.562-0.2630.9910.2010.201
height_cm0.0000.042-0.2290.0000.343-0.1700.229-0.035-0.080-0.1000.1530.2501.0001.0000.1730.0230.0850.426-0.1150.3460.374-0.1800.3600.3800.380
height_in0.0000.042-0.2290.0000.343-0.1700.242-0.035-0.080-0.1000.1530.2501.0001.0000.0630.0230.0850.426-0.1150.3460.374-0.1800.3600.3800.380
political_party0.0000.0000.0000.0000.3120.0000.0000.1160.1800.0530.0000.3910.1730.0631.0000.2730.0620.4380.2050.3570.0000.0000.2920.0000.000
presidency_begin_age0.000-0.004-0.0870.039-0.123-0.0060.000-0.2670.642-0.0960.118-0.0830.0230.0230.2731.0000.9110.123-0.166-0.0360.075-0.203-0.114-0.014-0.014
presidency_end_age0.0000.069-0.2660.144-0.172-0.0160.000-0.1980.653-0.0630.061-0.0480.0850.0850.0620.9111.0000.053-0.127-0.0980.062-0.226-0.0990.0040.004
term_begin_day0.269-0.087-0.0270.2770.593-0.0430.000-0.0580.1500.3930.2400.4230.4260.4260.4380.1230.0531.000-0.3170.5900.656-0.5270.5720.1770.177
term_begin_month0.3710.1640.0120.154-0.462-0.0440.000-0.138-0.210-0.022-0.080-0.114-0.115-0.1150.205-0.166-0.127-0.3171.000-0.461-0.4710.138-0.421-0.100-0.100
term_begin_year0.000-0.2070.0960.2800.9930.1230.154-0.165-0.0070.2540.0090.9890.3460.3460.357-0.036-0.0980.590-0.4611.0000.717-0.4431.0000.2790.279
term_end_day0.000-0.030-0.1310.0000.7200.0930.0000.0110.0700.4210.1960.5620.3740.3740.0000.0750.0620.656-0.4710.7171.000-0.4060.7140.2980.298
term_end_month0.2430.273-0.1260.000-0.421-0.0520.0860.157-0.582-0.2690.035-0.263-0.180-0.1800.000-0.203-0.226-0.5270.138-0.443-0.4061.000-0.448-0.075-0.075
term_end_year0.000-0.2350.0540.2730.9950.1400.164-0.166-0.0020.2610.0090.9910.3600.3600.292-0.114-0.0990.572-0.4211.0000.714-0.4481.0000.2970.297
weight_kg0.2590.1290.0490.0000.2730.7880.720-0.015-0.063-0.0560.0760.2010.3800.3800.000-0.0140.0040.177-0.1000.2790.298-0.0750.2971.0001.000
weight_lb0.2590.1290.0490.0000.2730.7880.720-0.015-0.063-0.0560.0760.2010.3800.3800.000-0.0140.0040.177-0.1000.2790.298-0.0750.2971.0001.000

Missing values

2025-06-26T04:06:22.393878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-26T04:06:22.556236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-26T04:06:22.795923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

nameheight_cmheight_inweight_kgweight_lbbody_mass_indexbody_mass_index_rangebirth_daybirth_monthbirth_yearbirth_datebirthplacebirth_statedeath_daydeath_monthdeath_yeardeath_datedeath_ageastrological_signterm_begin_dayterm_begin_monthterm_begin_yearterm_begin_dateterm_end_dayterm_end_monthterm_end_yearterm_end_datepresidency_begin_agepresidency_end_agepolitical_partycorrected_iq
0George Washington18874.079.417522.5Normal222173222-02-1732Westmoreland CountyVirginia14.012.01799.014-12-179967.0Pisces304178930-04-17894.03.01797.004-03-17975765.0Unaffiliated140.0
1John Adams17067.083.918529.0Overweight3010173530-10-1735BraintreeMassachusetts4.07.01826.004-07-182690.0Scorpio43179704-03-17974.03.01801.004-03-18016165.0Federalist155.0
2Thomas Jefferson18974.582.118123.0Normal134174313-04-1743ShadwellVirginia4.07.01826.004-07-182683.0Aries43180104-03-18014.03.01809.004-03-18095765.0Democratic-Republican160.0
3James Madison16364.055.312220.8Normal163175116-03-1751Port ConwayVirginia28.06.01836.028-06-183685.0Pisces43180904-03-18094.03.01817.004-03-18175765.0Democratic-Republican160.0
4James Monroe18372.085.718925.6Overweight284175828-04-1758Monroe HallVirginia4.07.01831.004-07-183173.0Taurus43181704-03-18174.03.01825.004-03-18255866.0Democratic-Republican139.0
5John Quincy Adams17167.592.120331.5Obese117176711-07-1767BraintreeMassachusetts23.02.01848.023-02-184880.0Cancer43182504-03-18254.03.01829.004-03-18295761.0Democratic-Republican175.0
6Andrew Jackson18573.063.514018.6Normal153176715-03-1767Waxhaws RegionSouth Carolina8.06.01845.008-06-184578.0Pisces43182904-03-18294.03.01837.004-03-18376169.0Democrat145.0
7Martin Van Buren16866.074.416426.4Overweight512178205-12-1782KinderhookNew York24.07.01862.024-07-186279.0Sagittarius43183704-03-18374.03.01841.004-03-18415458.0Democrat146.0
8William Henry Harrison17368.073.516224.6Normal92177309-02-1773Charles City CountyVirginia4.04.01841.004-04-184168.0Aquarius43184104-03-18414.04.01841.004-04-18416868.0Whig146.0
9John Tyler18372.072.616021.7Normal293179029-03-1790Charles City CountyVirginia18.01.01862.018-01-186271.0Aries44184104-04-18414.03.01845.004-03-18455154.0Whig148.0
nameheight_cmheight_inweight_kgweight_lbbody_mass_indexbody_mass_index_rangebirth_daybirth_monthbirth_yearbirth_datebirthplacebirth_statedeath_daydeath_monthdeath_yeardeath_datedeath_ageastrological_signterm_begin_dayterm_begin_monthterm_begin_yearterm_begin_dateterm_end_dayterm_end_monthterm_end_yearterm_end_datepresidency_begin_agepresidency_end_agepolitical_partycorrected_iq
35Richard Milhous Nixon18271.579.417524.0Normal91191309-01-1913Yorba LindaCalifornia22.04.01994.022-04-199481.0Capricorn201196920-01-19699.08.01974.009-08-19745661.0Republican143.0
36Gerald Rudolph Ford18372.086.219025.7Overweight147191314-07-1913OmahaNebraska26.012.02006.026-12-200693.0Cancer98197409-08-197420.01.01977.020-01-19776163.0Republican140.0
37James Earl Carter17769.580.317725.6Overweight110192401-10-1924PlainsGeorgiaNaNNaNNaNNaNNaNLibran201197720-01-197720.01.01981.020-01-19815256.0Democrat157.0
38Ronald Wilson Reagan18573.081.618023.8Normal62191106-02-1911TampicoIllinois5.06.02004.005-06-200493.0Aquarius201198120-01-198120.01.01989.020-01-19896977.0Republican142.0
39George Herbert Walker Bush18874.088.519525.0Overweight126192412-06-1924MiltonMassachusetts30.012.02018.030-12-201894.0Gemini201198920-01-198920.01.01993.020-01-19936468.0Republican143.0
40William Jefferson Clinton18874.0101.222328.6Overweight198194619-08-1946HopeArkansasNaNNaNNaNNaNNaNLeo201199320-01-199320.01.02001.020-01-20014654.0Democrat159.0
41George Walker Bush18271.584.418625.5Overweight66194606-06-1946New HavenConnecticutNaNNaNNaNNaNNaNGemini201200120-01-200120.01.02009.020-01-20095452.0Republican139.0
42Barack Hussein Obama18573.081.618023.8Normal48196104-08-1961HonoluluHawaiiNaNNaNNaNNaNNaNLeo201200920-01-200920.01.02017.020-01-20174755.0DemocratNaN
43Donald John Trump19175.0107.523729.5Overweight146194614-06-1946QueensNew YorkNaNNaNNaNNaNNaNGemini201201720-01-201720.01.02021.020-01-20217074.0RepublicanNaN
44Joseph Robinette Biden18271.580.717824.4Normal2011194220-11-1942ScrantonPennsylvaniaNaNNaNNaNNaNNaNScorpio201202120-01-2021NaNNaNNaNNaN78NaNDemocratNaN